Model-based Contact-rich Manipulation Planning

This repo provides implementation of model-based planners for contact-rich manipulation. The attached Deepnote project includes two notebooks, illustrating two planning algorithms on the Allegro hand dexterous manipulation example:

• A trajectory optimizer using iterative MPC (iMPC),
• A sampling-based planner capable of handling contact dynamics constraints.

Details of the planning algorithms can be found in

• Global Planning for Contact-Rich Manipulation via Local Smoothing of Quasi-dynamic Contact Models, currently under review.

Our quasidynamic simulator can be found on the tro2023 branch of the quasistatic_simulator repo:

• https://github.com/pangtao22/quasistatic_simulator/tree/tro2023